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1561 computation of denoising posterior precision matrix in jac method score fn iid #1636

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Merged
merged 4 commits into from
Aug 13, 2025
Merged

1561 computation of denoising posterior precision matrix in jac method score fn iid #1636

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6b26d1a
Fix
manuelgloeckler Aug 13, 2025
6bef849
Do not fully skip all iid_test but only what is currently adressed in…
manuelgloeckler Aug 13, 2025
3aab409
Additional skip for FMPE
manuelgloeckler Aug 13, 2025
a63e3b2
xfail
manuelgloeckler Aug 13, 2025
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2 changes: 1 addition & 1 deletion sbi/inference/potentials/score_fn_iid.py
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Expand Up @@ -727,7 +727,7 @@ def marginal_denoising_posterior_precision_est_fn(
std = self.vector_field_estimator.std_fn(time)
cov0 = std**2 * jac + torch.eye(d)[None, None, :, :]

denoising_posterior_precision = m**2 / std**2 + torch.inverse(cov0)
denoising_posterior_precision = m**2 / std**2 * torch.inverse(cov0)

return denoising_posterior_precision

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15 changes: 11 additions & 4 deletions tests/linearGaussian_vector_field_test.py
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Expand Up @@ -357,18 +357,21 @@ def test_vector_field_sde_ode_sampling_equivalence(vector_field_trained_model):
# TODO: Currently, c2st is too high for FMPE (e.g., > 3 number of observations),
# so some tests are skipped so far. This seems to be an issue with the
# neural network architecture and can be addressed in PR #1501
@pytest.mark.skip(
reason="c2st too high for some cases, has to be fixed in PR #1501 or #1544"
)
@pytest.mark.slow
@pytest.mark.parametrize(
"iid_method, num_trial",
[
pytest.param("fnpe", 3, id="fnpe-2trials"),
pytest.param(
"fnpe",
3,
id="fnpe-3trials",
marks=pytest.mark.xfail(reason="c2st to high, fixed in PR #1501/1544"),
),
pytest.param("gauss", 3, id="gauss-3trials"),
pytest.param("auto_gauss", 8, id="auto_gauss-8trials"),
pytest.param("auto_gauss", 16, id="auto_gauss-16trials"),
pytest.param("jac_gauss", 8, id="jac_gauss-8trials"),
pytest.param("jac_gauss", 16, id="jac_gauss-16trials"),
],
)
def test_vector_field_iid_inference(
Expand All @@ -377,6 +380,10 @@ def test_vector_field_iid_inference(
"""
Test whether NPSE and FMPE infers well a simple example with available ground truth.
"""
if vector_field_type == "fmpe":
# TODO: Remove on merge
pytest.xfail(reason="c2st to high, fixed in PR #1501/1544")

num_samples = 1000

# Extract data from fixture
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